Symbiodinium communities in reef corals of St. John, U.S.V.I. (2012)
Data analysis report
Ross Cunning
14 August, 2016
Overview
This project investigates Symbiodinium communities in scleractinian and milleporine corals sampled at various sites on the north and south shores of St. John, US Virgin Islands in August 2012. The ITS2 region of nrDNA isolated from coral samples was amplified and sequenced by paired-end 300 bp reads on an Illumina MiSeq platform. Sequence data is processed through a custom bioinformatic pipeline and resulting OTU tables are statistically analyzed to ask questions regarding diversity and community structure of Symbiodinium within and across coral species and between shores. The major aims of this work are to:
- characterize Symbiodinium communities in a range of coral species using high-throughput sequencing (many for the first time)
- forward novel bioinformatic (within-sample clustering) and statistical (beta diversity/distance to centroid, network analysis) approaches for the use of ITS2 data in Symbiodinium ecology.
Sampling locations in St. John
Bioinformatics
A custom pipeline is used here to process ITS2 sequence data. Briefly, paired reads were merged using illumina-utils and filtered to keep only those with 3 mismatches or less. Chimeras were then removed by usearch, and singletons were removed. From here, sequence data was processed using each of three approaches:
- 100% OTU clustering. All ITS2 sequences from all samples were clustered at 100% identity using uclust and assigned taxonomy by BLAST to the custom reference database.
- 97% OTU clustering. All ITS2 sequences from all samples were clustered at 97% identity using uclust and the most abundant sequence within each OTU was selected as the representative sequence and assigned taxonomy by BLAST to the custom reference database.
- 97% OTU clustering within samples. Sequences from each individual sample were clustered separately at 97% similarity and the most abundant sequence from each cluster was selected as the representative sequence and assigned taxonomy by BLAST to the custom reference database. OTUs with identical representative sequences were then merged across samples to create a global OTU table. By clustering at 97% within samples, as opposed to across the entire dataset, OTUs from different samples that have different representative sequences (i.e., different most abundant sequence within the 97% cluster) will remain separate even if these sequences are >97% similar to each other. This approach is used because Symbiodinium ITS2 sequences are know to be multi-copy and intragenomically variable, and distinct Symbiodinium types may contain some of the same ITS2 sequence variants in different relative proportions. Therefore, selecting the most abundant sequence variant within a sample prevents a potentially distinct Symbiodinium type from being collapsed into an OTU with a different representative sequence that happens to be more abundant in other samples within the dataset. For more discussion on this approach, see this entry in my lab notebook.
OTU tables and taxonomic assignments from each of these bioinformatic approaches are used in comparative downstream analyses.
Data pre-processing
Import dataset
The phyloseq package is used here to combine the OTU table, taxonomic assignments, and sample metadata into a single R data object (class ‘phyloseq’) to facilitate downstream analyses.
# Import OTU tables
OTU97 <- otu_table(read.table("data/97_otus.tsv", header=T, check.names=F, row.names=1,
skip=1, sep="\t", comment.char=""), taxa_are_rows=T)
OTU97bs <- otu_table(read.table("data/97_otus_bysample.tsv", header=T, check.names=F, row.names=1,
skip=1, sep="\t", comment.char=""), taxa_are_rows=T)
OTU100 <- otu_table(read.table("data/100_otus.tsv", header=T, check.names=F, row.names=1,
skip=1, sep="\t", comment.char=""), taxa_are_rows=T)
# Import taxonomy
TAX97 <- tax_table(as.matrix(read.table("data/97_otus_tax_assignments.txt", sep="\t", row.names=2,
col.names=c("Clade", "OTU.ID", "Taxonomy", "Eval", "Subtype"))))
TAX97bs <- tax_table(as.matrix(read.table("data/97_otus_bysample_tax_assignments.txt", sep="\t", row.names=2,
col.names=c("Clade", "OTU.ID", "Taxonomy", "Eval", "Subtype"))))
TAX100 <- tax_table(as.matrix(read.table("data/100_otus_tax_assignments.txt", sep="\t", row.names=2,
col.names=c("Clade", "OTU.ID", "Taxonomy", "Eval", "Subtype"))))
# Import sample data
SAM <- sample_data(read.delim("data/mapping_file.txt", sep="\t", header=T, row.names=1))
# Build phyloseq objects
phy97 <- phyloseq(OTU97, TAX97, SAM)
phy97bs <- phyloseq(OTU97bs, TAX97bs, SAM)
phy100 <- phyloseq(OTU100, TAX100, SAM)Filter dataset
# Filter OTUs by minimum count
# Set threshold count
n <- 10
# Identify OTUs below threshold count
taxa97 <- taxa_sums(phy97)[which(taxa_sums(phy97) >= n)]
taxa97bs <- taxa_sums(phy97bs)[which(taxa_sums(phy97bs) >= n)]
taxa100 <- taxa_sums(phy100)[which(taxa_sums(phy100) >= n)]
# Remove taxa below threshold count
phy97.f <- prune_taxa(names(taxa97), phy97)
phy97bs.f <- prune_taxa(names(taxa97bs), phy97bs)
phy100.f <- prune_taxa(names(taxa100), phy100)
# Filter samples by minimum count
# Set threshold number of reads
sn <- 200
# Remove samples with fewer reads than threshold
phy97.f <- prune_samples(sample_sums(phy97.f)>=sn, phy97.f)
phy97bs.f <- prune_samples(sample_sums(phy97bs.f)>=sn, phy97bs.f)
phy100.f <- prune_samples(sample_sums(phy100.f)>=sn, phy100.f)- Minimum count to retain OTU: 10
- Minimum count to retain sample: 200
Descriptive statistics
Filtered dataset
# Compute summary statistics
stats97.f <- data.frame(`97% OTUs`=t(phystats(phy97.f)), check.names=F)
stats97bs.f <- data.frame(`97% within-sample OTUs`=t(phystats(phy97bs.f)), check.names=F)
stats100.f <- data.frame(`100% OTUs`=t(phystats(phy100.f)), check.names=F)
# Create and plot histograms
taxhist97 <- hist(log10(taxa_sums(phy97.f)), plot=F)
taxhist97bs <- hist(log10(taxa_sums(phy97bs.f)), plot=F)
taxhist100 <- hist(log10(taxa_sums(phy100.f)), plot=F)
samhist97 <- hist(log10(sample_sums(phy97.f)), plot=F)
samhist97bs <- hist(log10(sample_sums(phy97bs.f)), plot=F)
samhist100 <- hist(log10(sample_sums(phy100.f)), plot=F)
par(mfrow=c(2, 3), mar=c(3,3,1,1))
plot(taxhist97, col="black", main="97% OTU counts", xlim=c(0, 6), las=1, mgp=c(2,0.5,0),
xlab="No. sequences (log10)", ylab="No. OTUs", cex.lab=0.75, cex.axis=0.75)
plot(taxhist97bs, col="black", main="97% OTU counts", xlim=c(0, 6), las=1, mgp=c(2,0.5,0),
xlab="No. sequences (log10)", ylab="No. OTUs", cex.lab=0.75, cex.axis=0.75)
plot(taxhist100, col="black", main="100% OTU counts", xlim=c(0, 6), las=1, mgp=c(2,0.5,0),
xlab="No. sequences (log10)", ylab="No. OTUs", cex.lab=0.75, cex.axis=0.75)
plot(samhist97, col="black", main="97% OTU reads per sample", xlim=c(0, 6), las=1, mgp=c(2,0.5,0),
xlab="No. sequences (log10)", ylab="No. samples", cex.lab=0.75, cex.axis=0.75)
plot(samhist97bs, col="black", main="97% OTU reads per sample", xlim=c(0, 6), las=1, mgp=c(2,0.5,0),
xlab="No. sequences (log10)", ylab="No. samples", cex.lab=0.75, cex.axis=0.75)
plot(samhist100, col="black", main="100% OTU reads per sample", xlim=c(0, 6), las=1, mgp=c(2,0.5,0),
xlab="No. sequences (log10)", ylab="No. samples", cex.lab=0.75, cex.axis=0.75)
# Create stats table
knitr::kable(cbind(stats97.f, stats97bs.f, stats100.f))| 97% OTUs | 97% within-sample OTUs | 100% OTUs | |
|---|---|---|---|
| Total count in OTU table | 1492454 | 1492337 | 944975 |
| Number of OTUs | 102 | 117 | 4727 |
| Range of OTU counts | 10 - 739699 | 0 - 474176 | 10 - 171442 |
| Number of singleton OTUs | 0 | 1 | 0 |
| Number of samples | 80 | 80 | 80 |
| Range of reads per sample | 706 - 169958 | 707 - 169964 | 485 - 97036 |
| Arithmetic mean (±SD) reads per sample | 18655 ± 21113 | 18654 ± 21114 | 11812 ± 12750 |
| Geometric mean (±SD) reads per sample | 13165 ± 2 | 13161 ± 2 | 8152 ± 2 |
Raw dataset
# Compute summary statistics
stats97 <- data.frame(`97% OTUs`=t(phystats(phy97)), check.names=F)
stats97bs <- data.frame(`97% within-sample OTUs`=t(phystats(phy97bs)), check.names=F)
stats100 <- data.frame(`100% OTUs`=t(phystats(phy100)), check.names=F)
# Create and plot histograms
taxhist97 <- hist(log10(taxa_sums(phy97)), plot=F)
samhist97 <- hist(log10(sample_sums(phy97)), plot=F)
taxhist97bs <- hist(log10(taxa_sums(phy97bs)), plot=F)
samhist97bs <- hist(log10(sample_sums(phy97bs)), plot=F)
taxhist100 <- hist(log10(taxa_sums(phy100)), plot=F)
samhist100 <- hist(log10(sample_sums(phy100)), plot=F)
par(mfrow=c(2, 3), mar=c(3,3,1,1))
plot(taxhist97, col="black", main="97% OTU counts", xlim=c(0, 6), las=1, mgp=c(2,0.5,0),
xlab="No. sequences (log10)", ylab="No. OTUs", cex.lab=0.75, cex.axis=0.75)
plot(taxhist97bs, col="black", main="97% within-sample OTU counts", xlim=c(0, 6), las=1, mgp=c(2,0.5,0),
xlab="No. sequences (log10)", ylab="No. OTUs", cex.lab=0.75, cex.axis=0.75)
plot(taxhist100, col="black", main="100% OTU counts", xlim=c(0, 6), las=1, mgp=c(2,0.5,0),
xlab="No. sequences (log10)", ylab="No. OTUs", cex.lab=0.75, cex.axis=0.75)
plot(samhist97, col="black", main="97% OTU reads per sample", xlim=c(0, 6), las=1, mgp=c(2,0.5,0),
xlab="No. sequences (log10)", ylab="No. samples", cex.lab=0.75, cex.axis=0.75)
plot(samhist97bs, col="black", main="97% within-sample OTU reads per sample", xlim=c(0, 6), las=1, mgp=c(2,0.5,0),
xlab="No. sequences (log10)", ylab="No. samples", cex.lab=0.75, cex.axis=0.75)
plot(samhist100, col="black", main="100% OTU reads per sample", xlim=c(0, 6), las=1, mgp=c(2,0.5,0),
xlab="No. sequences (log10)", ylab="No. samples", cex.lab=0.75, cex.axis=0.75)
# Create stats table
knitr::kable(cbind(stats97, stats97bs, stats100))| 97% OTUs | 97% within-sample OTUs | 100% OTUs | |
|---|---|---|---|
| Total count in OTU table | 1492736 | 1492692 | 1056483 |
| Number of OTUs | 135 | 174 | 41449 |
| Range of OTU counts | 2 - 739709 | 2 - 474177 | 2 - 171487 |
| Number of singleton OTUs | 0 | 0 | 0 |
| Number of samples | 84 | 84 | 84 |
| Range of reads per sample | 3 - 169977 | 3 - 169975 | 1 - 112823 |
| Arithmetic mean (±SD) reads per sample | 17770 ± 20982 | 17770 ± 20982 | 12577 ± 14480 |
| Geometric mean (±SD) reads per sample | 9427 ± 5 | 9400 ± 5 | 6451 ± 6 |
Transform count data
Count data are transformed to both relative abundance (proportions) and square-root proportions for downstream statistical analyses.
# Convert to proportion (relative abundance)
phy97.f.p <- transform_sample_counts(phy97.f, function(x) x/sum(x))
phy97bs.f.p <- transform_sample_counts(phy97bs.f, function(x) x/sum(x))
phy100.f.p <- transform_sample_counts(phy100.f, function(x) x/sum(x))
# Apply transformation function
transform <- function(x) sqrt(x/sum(x)) # Set transformation function
phy97.f.t <- transform_sample_counts(phy97.f, transform) # Transform data
phy97bs.f.t <- transform_sample_counts(phy97bs.f, transform)
phy100.f.t <- transform_sample_counts(phy100.f, transform)Symbiodinium in each coral
For each coral species, barplots are presented showing the relative abundance of OTUs obtained by 100%, 97%, and 97%-within-sample clustering. OTUs comprising more than 4% of a sample are labeled with the unique OTU number and the Symbiodinium subtype and NCBI GenBank accession number of the closest BLAST hit for that OTU in the reference database. OTU numbers and barplot colors are NOT comparable across the three clustering methods.
Diploria strigosa
# Create subsetted phyloseq objects for Diploria strigosa
dstr97.f.p <- subset_samples(phy97.f.p, Species=="strigosa")
dstr97bs.f.p <- subset_samples(phy97bs.f.p, Species=="strigosa")
dstr100.f.p <- subset_samples(phy100.f.p, Species=="strigosa")
# Plot custom barplots for Diploria strigosa
par(mfrow=c(3,1), mar=c(4,4,2,0), mgp=c(0,0.5,0), lwd=0.1)
otubarplot(dstr97.f.p, main="97% OTUs")
otubarplot(dstr97bs.f.p, main="97% within-sample OTUs")
otubarplot(dstr100.f.p, main="100% OTUs")
dstr.net <- makenet(dstr97bs.f.p, 0)
set.seed(54538)
plotnet(dstr.net)
Network analysis for D. strigosa
Number of OTUs: 40
White circles represent coral samples, colored circles represent Symbiodinium OTUs (97% within-sample). Thickness of lines corresponds to the relative abundance of an OTU within a sample.
Porites furcata
# Create subsetted phyloseq objects for Porites furcata
pfur97.f.p <- subset_samples(phy97.f.p, Species=="furcata")
pfur97bs.f.p <- subset_samples(phy97bs.f.p, Species=="furcata")
pfur100.f.p <- subset_samples(phy100.f.p, Species=="furcata")
# Plot custom barplots for Porites furcata
par(mfrow=c(3,1), mar=c(4,4,2,0), mgp=c(0,0.5,0), lwd=0.1)
otubarplot(pfur97.f.p, main="97% OTUs")
otubarplot(pfur97bs.f.p, main="97% within-sample OTUs")
otubarplot(pfur100.f.p, main="100% OTUs")
# Network analysis
pfur.net <- makenet(pfur97bs.f.p, 0)
par(mar=c(0,0,0,0))
set.seed(54538)
plotnet(pfur.net)
Network analysis for P. furcata
Number of OTUs: 24
White circles represent coral samples, colored circles represent Symbiodinium OTUs (97% within-sample). Thickness of lines corresponds to the relative abundance of an OTU within a sample.
Orbicella annularis
# Create subsetted phyloseq objects for Orbicella annularis
oann97.f.p <- subset_samples(phy97.f.p, Species=="annularis")
oann97bs.f.p <- subset_samples(phy97bs.f.p, Species=="annularis")
oann100.f.p <- subset_samples(phy100.f.p, Species=="annularis")
# Plot custom barplots for Orbicella annularis
par(mfrow=c(3,1), mar=c(4,4,2,0), mgp=c(0,0.5,0), lwd=0.1)
otubarplot(oann97.f.p, main="97% OTUs")
otubarplot(oann97bs.f.p, main="97% within-sample OTUs")
otubarplot(oann100.f.p, main="100% OTUs")
# Network analysis
oann.net <- makenet(oann97bs.f.p, 0)
par(mar=c(0,0,0,0))
set.seed(54538)
plotnet(oann.net)
Network analysis for O. annularis
Number of OTUs: 6
White circles represent coral samples, colored circles represent Symbiodinium OTUs (97% within-sample). Thickness of lines corresponds to the relative abundance of an OTU within a sample.
Millepora alcicornis
# Create subsetted phyloseq objects for Millepora alcicornis
malc97.f.p <- subset_samples(phy97.f.p, Species=="alcicornis")
malc97bs.f.p <- subset_samples(phy97bs.f.p, Species=="alcicornis")
malc100.f.p <- subset_samples(phy100.f.p, Species=="alcicornis")
# Plot custom barplots for Millepora alcicornis
par(mfrow=c(3,1), mar=c(4,4,2,0), mgp=c(0,0.5,0), lwd=0.1)
otubarplot(malc97.f.p, main="97% OTUs")
otubarplot(malc97bs.f.p, main="97% within-sample OTUs")
otubarplot(malc100.f.p, main="100% OTUs")
# Network analysis
malc.net <- makenet(malc97bs.f.p, 0)
par(mar=c(0,0,0,0))
set.seed(54538)
plotnet(malc.net)
Network analysis for M. alcicornis
Number of OTUs: 18
White circles represent coral samples, colored circles represent Symbiodinium OTUs (97% within-sample). Thickness of lines corresponds to the relative abundance of an OTU within a sample.
Siderastrea siderea
# Create subsetted phyloseq objects for Siderastrea siderea
ssid97.f.p <- subset_samples(phy97.f.p, Species=="siderea")
ssid97bs.f.p <- subset_samples(phy97bs.f.p, Species=="siderea")
ssid100.f.p <- subset_samples(phy100.f.p, Species=="siderea")
# Plot custom barplots for Siderastrea siderea
par(mfrow=c(3,1), mar=c(4,4,2,0), mgp=c(0,0.5,0), lwd=0.1)
otubarplot(ssid97.f.p, main="97% OTUs")
otubarplot(ssid97bs.f.p, main="97% within-sample OTUs")
otubarplot(ssid100.f.p, main="100% OTUs")
# Network analysis
ssid.net <- makenet(ssid97bs.f.p, 0)
par(mar=c(0,0,0,0))
set.seed(834)
plotnet(ssid.net)
Network analysis for S. siderea
Number of OTUs: 16
White circles represent coral samples, colored circles represent Symbiodinium OTUs (97% within-sample). Thickness of lines corresponds to the relative abundance of an OTU within a sample.
Favia fragum
# Create subsetted phyloseq objects for Favia fragum
ffra97.f.p <- subset_samples(phy97.f.p, Species=="fragum")
ffra97bs.f.p <- subset_samples(phy97bs.f.p, Species=="fragum")
ffra100.f.p <- subset_samples(phy100.f.p, Species=="fragum")
# Plot custom barplots for Favia fragum
par(mfrow=c(3,1), mar=c(4,4,2,0), mgp=c(0,0.5,0), lwd=0.1)
otubarplot(ffra97.f.p, main="97% OTUs")
otubarplot(ffra97bs.f.p, main="97% within-sample OTUs")
otubarplot(ffra100.f.p, main="100% OTUs")
# Network analysis
ffra.net <- makenet(ffra97bs.f.p, 0)
par(mar=c(0,0,0,0))
set.seed(54538)
plotnet(ffra.net)
Network analysis for F. fragum
Number of OTUs: 14
White circles represent coral samples, colored circles represent Symbiodinium OTUs (97% within-sample). Thickness of lines corresponds to the relative abundance of an OTU within a sample.
Siderastrea radians
# Create subsetted phyloseq objects for Siderastrea radians
srad97.f.p <- subset_samples(phy97.f.p, Species=="radians")
srad97bs.f.p <- subset_samples(phy97bs.f.p, Species=="radians")
srad100.f.p <- subset_samples(phy100.f.p, Species=="radians")
# Plot custom barplots for Siderastrea radians
par(mfrow=c(3,1), mar=c(4,4,2,0), mgp=c(0,0.5,0), lwd=0.1)
otubarplot(srad97.f.p, main="97% OTUs")
otubarplot(srad97bs.f.p, main="97% within-sample OTUs")
otubarplot(srad100.f.p, main="100% OTUs")
# Network analysis
srad.net <- makenet(srad97bs.f.p, 0)
par(mar=c(0,0,0,0))
set.seed(54538)
plotnet(srad.net)
Network analysis for S. radians
Number of OTUs: 14
White circles represent coral samples, colored circles represent Symbiodinium OTUs (97% within-sample). Thickness of lines corresponds to the relative abundance of an OTU within a sample.
Porites astreoides
# Create subsetted phyloseq objects for Porites astreoides
past97.f.p <- subset_samples(phy97.f.p, Species=="astreoides")
past97bs.f.p <- subset_samples(phy97bs.f.p, Species=="astreoides")
past100.f.p <- subset_samples(phy100.f.p, Species=="astreoides")
# Plot custom barplots for Porites astreoides
par(mfrow=c(3,1), mar=c(4,4,2,0), mgp=c(0,0.5,0), lwd=0.1)
otubarplot(past97.f.p, main="97% OTUs")
otubarplot(past97bs.f.p, main="97% within-sample OTUs")
otubarplot(past100.f.p, main="100% OTUs")
# Network analysis
past.net <- makenet(past97bs.f.p, 0)
par(mar=c(0,0,0,0))
set.seed(54538)
plotnet(past.net)
Network analysis for P. astreoides
Number of OTUs: 8
White circles represent coral samples, colored circles represent Symbiodinium OTUs (97% within-sample). Thickness of lines corresponds to the relative abundance of an OTU within a sample.
Dendrogyra cylindrus
# Create subsetted phyloseq objects for Dendrogyra cylindrus
dcyl97.f.p <- subset_samples(phy97.f.p, Species=="cylindrus")
dcyl97bs.f.p <- subset_samples(phy97bs.f.p, Species=="cylindrus")
dcyl100.f.p <- subset_samples(phy100.f.p, Species=="cylindrus")
# Plot custom barplots for Dendrogyra cylindrus
par(mfrow=c(3,1), mar=c(4,4,2,0), mgp=c(0,0.5,0), lwd=0.1)
otubarplot(dcyl97.f.p, main="97% OTUs")
otubarplot(dcyl97bs.f.p, main="97% within-sample OTUs")
otubarplot(dcyl100.f.p, main="100% OTUs")
# Network analysis
dcyl.net <- makenet(dcyl97bs.f.p, 0)
par(mar=c(0,0,0,0))
set.seed(54538)
plotnet(dcyl.net)
Network analysis for D. cylindrus
Number of OTUs: 17
White circles represent coral samples, colored circles represent Symbiodinium OTUs (97% within-sample). Thickness of lines corresponds to the relative abundance of an OTU within a sample.
Montastraea cavernosa
# Create subsetted phyloseq objects for Montastraea cavernosa
mcav97.f.p <- subset_samples(phy97.f.p, Species=="cavernosa")
mcav97bs.f.p <- subset_samples(phy97bs.f.p, Species=="cavernosa")
mcav100.f.p <- subset_samples(phy100.f.p, Species=="cavernosa")
# Plot custom barplots for Montastraea cavernosa
par(mfrow=c(3,1), mar=c(4,4,2,0), mgp=c(0,0.5,0), lwd=0.1)
otubarplot(mcav97.f.p, main="97% OTUs")
otubarplot(mcav97bs.f.p, main="97% within-sample OTUs")
otubarplot(mcav100.f.p, main="100% OTUs")
# Network analysis
mcav.net <- makenet(mcav97bs.f.p, 0)
par(mar=c(0,0,0,0))
set.seed(54538)
plotnet(mcav.net)
Network analysis for M. cavernosa
Number of OTUs: 6
White circles represent coral samples, colored circles represent Symbiodinium OTUs (97% within-sample). Thickness of lines corresponds to the relative abundance of an OTU within a sample.
All species barplot
Here the composition of each sample is plotted as a horizontal bar, sorted by species and shore. Each segment of the bar represents an individual OTU and is colored by clade. This plot is a nice overview of the whole dataset but only provides coarse information at the clade level.
par(mfrow=c(1,1), mar=c(2, 1.5, 2, 5), lwd=0.1, cex=0.7)
# Plot composition of 97% within-sample OTUs colored by clade
composition(phy97bs.f.p, col=taxcolors[factor(data.frame(tax_table(phy97bs.f.p))[order(data.frame(tax_table(phy97bs.f.p))$Subtype), ]$Clade, levels=c("A","B","C","D","F","G"))], legend=T)
# Plot composition of 97% within-sample OTUs colored by OTU
#composition(phy97bs.f.p, col=rainbow(ntaxa(phy97bs.f.p)), legend=F)All species network analysis
In this network, each coral species is collapsed into a single node (white squares), and connected to every Symbiodinium OTU (colored circles) that comprises at least 1% relative abundance within at least one individual of that species. Thus, very low abundance OTUs are not represented. The thickness of connections between coral species and OTUs is scaled by the relative proportion of samples of that species in which that OTU was present at ≥ 1% relative abundance. The size of Symbiodinium OTU nodes in the network is scaled by the number of species in which they occur.
# Get network with one node for each species.
# Edge weight is the mean relative abundance of OTU.
# Convert OTU table to "edges" table (weight = relative abundance)
otudf <- data.frame(t(otu_table(phy97bs.f.p)), check.names=F)
# Average relative abundance of OTU within species
#sp.ag <- aggregate(otudf, by=list(Species=data.frame(sample_data(phy97.f.p))$Species), FUN=mean)
# Percent of samples of species that contain OTU at more than 1% relative abundance
sp.ag <- aggregate(otudf, by=list(Species=data.frame(sample_data(phy97bs.f.p))$Species),
FUN=function(x) length(x[x>0.01])/length(x))
rownames(sp.ag) <- sp.ag$Species
sp.ag <- sp.ag[, -1]
edges <- setNames(melt(as.matrix(sp.ag)), nm=c("source", "target", "weight")) # Melt data
edges <- droplevels(edges[edges$weight>0, ])
# Create "nodes" table from sample and tax data
sdf <- data.frame(id=levels(data.frame(sample_data(phy97bs.f.p))$Species))
tdf <- data.frame(tax_table(phy97bs.f.p))[rownames(data.frame(tax_table(phy97bs.f.p))) %in% edges$target, ]
tdf$id <- rownames(tdf)
nodes <- merge(sdf, tdf, all=T)
nodes$type <- ifelse(is.na(nodes$Clade), 1, 2)
# Create network
net <- graph_from_data_frame(d=edges, vertices=nodes, directed=F)
# Best one for now ---------
V(net)$shape <- c("square", "circle")[factor(V(net)$type)]
V(net)$size <- ifelse(is.na(V(net)$Clade), 15, 10*sqrt(degree(net))) #5*sqrt(degree(net))
V(net)$label <- ifelse(is.na(V(net)$Clade), names(V(net)),
unlist(lapply(strsplit(V(net)$Subtype, split="_"), "[", 1)))
V(net)$label <- str_replace_all(V(net)$label, c("alcicornis"="M.alc.", "annularis"="O.ann",
"astreoides"="P.ast.", "cavernosa"="M.cav.",
"cylindrus"="D.cyl.", "fragum"="F.fra.",
"furcata"="P.fur.", "radians"="S.rad.",
"siderea"="S.sid.", "strigosa"="D.str."))
V(net)$color <- ifelse(is.na(V(net)$Clade), "white",
taxcolors[factor(V(net)$Clade, levels=c("A","B","C","D","F","G"))])
E(net)$color <- "gray60"
E(net)$width <- 15 * E(net)$weight
set.seed(12347)
set.seed(12364)
set.seed(12374)
l <- layout.fruchterman.reingold(net)
l <- norm_coords(l, ymin=-1, ymax=1, xmin=-1, xmax=1)
par(mar=c(0,0,0,0))
plot(net, rescale=F, layout=l*1, edge.curved=0.1, vertex.label.cex=V(net)$size/20,
vertex.label.color="black")
#legend(x=-1.05, y=1.1, c("CladeA", "CladeB", "CladeC", "CladeD", "CladeF", "CladeG"), pch=21,
# col="#777777", pt.bg=taxcolors, pt.cex=1.1, cex=.6, bty="n", ncol=2, text.width=0.2)Differences between species
Whether Symbiodinium communities differ among species is evaluated using permutational analysis of variance (PERMANOVA).
# PERMANOVA for difference among species
sppdiffs <- function(phy) {
permanova.spp.t <- adonis(phyloseq::distance(phy, "bray") ~ Species,
data=as(sample_data(phy), "data.frame"), permutations=9999)
permanova.spp.t
# Pairwise PERMANOVA tests for differences between species
dmat <- as(phyloseq::distance(phy, "bray"), "matrix")
df <- as(sample_data(phy), "data.frame")
pairs <- data.frame(t(combn(levels(df$Species), 2)), stringsAsFactors=F)
p.pairs <- setNames(rep(NA, nrow(pairs)), with(pairs, paste(X1, X2, sep='-')))
for (i in 1:nrow(pairs)) {
dfs <- subset(df, Species %in% c(pairs[i,1], pairs[i,2]))
dmats <- dmat[rownames(dfs), rownames(dfs)]
set.seed(152470)
permanova <- adonis(dmats ~ Species, data=dfs, permutations=999)
p.pairs[i] <- permanova$aov.tab$`Pr(>F)`[1]
}
# Return letters signifying differences
return(multcompLetters(p.pairs))
}
sppdiffs97 <- sppdiffs(phy97.f.t)
sppdiffs97bs <- sppdiffs(phy97bs.f.t)
sppdiffs100 <- sppdiffs(phy100.f.t)
sppdiffs <- data.frame("97% within-sample OTUs"=sppdiffs97bs$Letters,
"100% OTUs"=sppdiffs100$Letters,
"97% OTUs"=sppdiffs97$Letters, check.names=F)
knitr::kable(sppdiffs, caption="Species not sharing a letter are significantly different (p < 0.05)")| 97% within-sample OTUs | 100% OTUs | 97% OTUs | |
|---|---|---|---|
| alcicornis | ab | a | a |
| annularis | ab | b | bc |
| astreoides | c | c | d |
| cavernosa | d | d | e |
| cylindrus | a | e | f |
| fragum | ab | f | a |
| furcata | e | g | b |
| radians | f | h | g |
| siderea | d | d | g |
| strigosa | b | b | ac |
Differences between shores
Whether Symbiodinium communities differ between north vs. south shores within species is tested here using PERMANOVA.
97% within-sample OTUs
set.seed(43789)
shorestats97bs <- perms(phy97bs.f.t, group="Species", trt="Shore")
knitr::kable(shorestats97bs, row.names=F,
caption="97% within-sample OTU data: differences within and between shore for each species")| Species | n | overall | within | between | R2 | bd | p |
|---|---|---|---|---|---|---|---|
| alcicornis | 10 | 0.63 | 0.68 | 0.59 | 0.04 | 0.97 | 0.85 |
| annularis | 4 | 0.58 | 0.58 | NaN | NA | NA | NA |
| astreoides | 5 | 0.06 | 0.06 | NaN | NA | NA | NA |
| cavernosa | 7 | 0.01 | 0.02 | 0.01 | 0.16 | 0.39 | 0.67 |
| cylindrus | 8 | 0.13 | 0.14 | 0.13 | 0.14 | 0.54 | 0.62 |
| fragum | 8 | 0.27 | 0.33 | 0.22 | 0.09 | 0.48 | 0.79 |
| furcata | 9 | 0.71 | 0.56 | 0.84 | 0.36 | 0.81 | 0.05 |
| radians | 10 | 0.10 | 0.10 | 0.10 | 0.12 | 0.85 | 0.37 |
| siderea | 10 | 0.52 | 0.53 | 0.51 | 0.09 | 0.57 | 0.48 |
| strigosa | 9 | 0.81 | 0.76 | 0.86 | 0.23 | 0.23 | 0.09 |
100% OTUs
set.seed(43789)
shorestats100 <- perms(phy100.f.t, group="Species", trt="Shore")
knitr::kable(shorestats100, row.names=F,
caption="100% OTU data: differences within and between shore for each species")| Species | n | overall | within | between | R2 | bd | p |
|---|---|---|---|---|---|---|---|
| alcicornis | 10 | 0.64 | 0.65 | 0.64 | 0.10 | 0.57 | 0.54 |
| annularis | 4 | 0.73 | 0.73 | NaN | NA | NA | NA |
| astreoides | 5 | 0.41 | 0.41 | NaN | NA | NA | NA |
| cavernosa | 7 | 0.45 | 0.45 | 0.46 | 0.19 | 0.95 | 0.29 |
| cylindrus | 8 | 0.43 | 0.38 | 0.48 | 0.21 | 0.03 | 0.06 |
| fragum | 8 | 0.55 | 0.57 | 0.53 | 0.13 | 0.30 | 0.51 |
| furcata | 9 | 0.70 | 0.55 | 0.81 | 0.45 | 0.39 | 0.01 |
| radians | 10 | 0.40 | 0.40 | 0.39 | 0.07 | 0.50 | 0.92 |
| siderea | 10 | 0.67 | 0.68 | 0.66 | 0.10 | 0.45 | 0.49 |
| strigosa | 9 | 0.81 | 0.76 | 0.85 | 0.22 | 0.04 | 0.09 |
97% OTUs
set.seed(43789)
shorestats97 <- perms(phy97.f.t, group="Species", trt="Shore")
knitr::kable(shorestats97, row.names=F,
caption="97% OTU data: differences within and between shore for each species")| Species | n | overall | within | between | R2 | bd | p |
|---|---|---|---|---|---|---|---|
| alcicornis | 10 | 0.13 | 0.13 | 0.13 | 0.11 | 0.17 | 0.44 |
| annularis | 4 | 0.59 | 0.59 | NaN | NA | NA | NA |
| astreoides | 5 | 0.07 | 0.07 | NaN | NA | NA | NA |
| cavernosa | 7 | 0.13 | 0.12 | 0.13 | 0.26 | 0.24 | 0.08 |
| cylindrus | 8 | 0.13 | 0.10 | 0.14 | 0.27 | 0.20 | 0.04 |
| fragum | 8 | 0.22 | 0.26 | 0.18 | 0.08 | 0.40 | 0.84 |
| furcata | 9 | 0.54 | 0.37 | 0.67 | 0.53 | 0.20 | 0.05 |
| radians | 10 | 0.13 | 0.14 | 0.13 | 0.06 | 0.96 | 0.86 |
| siderea | 10 | 0.30 | 0.28 | 0.32 | 0.21 | 0.19 | 0.02 |
| strigosa | 9 | 0.42 | 0.41 | 0.43 | 0.21 | 0.26 | 0.32 |
Beta diversity
Beta diversity is evaluated here as the multivariate dispersion of samples within a coral species. Principal coordinate analysis on Bray-Curtis dissimilarities is used to calculate average distance-to-centroid values for each species group, which are then compared statistically by a permutation test. This analysis is implemented using betadisper() in the vegan package, based on Anderson (2006). In this context, beta diveristy is analogous to ‘flexibility’ in symbiosis – how different can corals of the same species be in terms of their symbiont communities?
97% within-sample OTUs
betad97bs <- betad(phy97bs.f.t, group="Species")
with(betad97bs$sambdsumm.ord, {
plot(mean, type="n", main="97% within-sample OTUs",
ylim=c(0, 0.75), ylab="Distance to centroid", xaxt="n", xlab="")
arrows(1:10, mean - se, 1:10, mean + se, length=0.05, angle=90, code=3)
points(1:10, mean, cex=1, pch=21, bg="white")
text(1:10, par("usr")[3]-0.025, srt=45, adj=1, xpd=T, cex=0.75,
labels=levels(SAM$Species)[order(betad97bs$sambdsumm$mean, decreasing=T)])
text(1:10, mean + se + 0.03, labels=betad97bs$saml$Letters[as.character(group)], cex=0.5)
})
100% OTUs
betad100 <- betad(phy100.f.t, group="Species")
with(betad100$sambdsumm.ord, {
plot(mean, type="n", main="100% OTUs",
ylim=c(0, 0.75), ylab="Distance to centroid", xaxt="n", xlab="")
arrows(1:10, mean - se, 1:10, mean + se, length=0.05, angle=90, code=3)
points(1:10, mean, cex=1, pch=21, bg="white")
text(1:10, par("usr")[3]-0.025, srt=45, adj=1, xpd=T, cex=0.75,
labels=levels(SAM$Species)[order(betad100$sambdsumm$mean, decreasing=T)])
text(1:10, mean + se + 0.03, labels=betad100$saml$Letters[as.character(group)], cex=0.5)
})
97% OTUs
betad97 <- betad(phy97.f.t, group="Species")
# Figure: Distance to centroids
with(betad97$sambdsumm.ord, {
plot(mean, type="n", main="97% OTUs",
ylim=c(0, 0.75), ylab="Distance to centroid", xaxt="n", xlab="")
arrows(1:10, mean - se, 1:10, mean + se, length=0.05, angle=90, code=3)
points(1:10, mean, cex=1, pch=21, bg="white")
text(1:10, par("usr")[3]-0.025, srt=45, adj=1, xpd=T, cex=0.75,
labels=levels(SAM$Species)[order(betad97$sambdsumm$mean, decreasing=T)])
text(1:10, mean + se + 0.03, labels=betad97$saml$Letters[as.character(group)], cex=0.5)
})